Final-Project-Report.Rmd

---
title: "STAT 447 Final Project Report"
author: 
  - "Nicholas Wong (nwong27)"
  - "Sam Hwang (shwang62)"
  - "Akhil Gogineni (vg23)"
date: "April 28, 2024"
output:
  html_document:
    df_print: paged
  pdf_document: default
  word_document: default
---

```{r Setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Purpose 
The purpose of this application is to analyze the data set of course grades from the University of Washington Seattle. For analysis, 
we chose a dataset of class information for each class taught at the University of Washington Seattle from Fall $2010$ to Fall $2015.$ We were particularly interested in the breakdown of the letter grading within each class and performed exploratory data analysis, 
like top $10$ best and worst performing classes in each year. This data is anonymized and can be manipulated to highlight features which can be important to current University of Washington Seattle students. Using our visualizations and this data, the user can discover which classes have a higher average GPA when stratified by year. Through the Shiny app, the users have the ability to dynamically explore how the average GPA for certain classes, taught by certain instructors, has changed over the specified years. In doing so, the linear regression analysis we provided helps show the trend of GPA over time, illustrating improvements and declines in class performance.

# Data 
The data for this analysis comes from a Final Project students from the University of Washington Seattle worked on in a course called INFO 201: Technical Foundations of Informatics. The dataset is primarily divided into several columns which consists of the Year, Quarter, Class, Title, Instructor, Student Count, Average GPA, and multiple columns for letter grades (A, A-, B+, B, B-, C+, C, C-, D+, D, D-, F, and W). We decided to have the app be filtered mainly by $3$ of these columns, which are the Class, Instructor, and Year. The Class would be conditionally dependent on the Year, and the Instructor would be conditionally dependent on both the Class and Year.

# Interactive Features
The app will feature several interactive components to aid in data visualization and analysis:

+ Dropdown Menus: Users can select the Year, Class, and Instructor from dropdown menus to filter the data set accordingly.

+ Dynamic Plots: Upon selection, dynamic plots will update to show the distribution of grades for the selected class, including histograms and box plots.

+ Summary Statistics: A dynamic text output will display summary statistics like mean, median, and mode of the grades for the selected filters.

# Conclusion
This project allows University of Washington Seattle students to better understand the grading patterns across different courses and instructors, potentially aiding in course selection and academic planning. The use of R Markdown and Shiny has enabled us to build a comprehensive, interactive tool which can evolve with further insights and data contributions.